When an AI agent makes the wrong decision, who gets fired?
As enterprises race to accelerate decision velocity, the next challenge isn’t model accuracy ... it’s delivering decision automation loops with accountability. There’s plenty of talk about agentic frameworks, LLMs, and semantics, but leaders need to know how to frame outcomes before they frame architectures. With only 21% of organizations fully embedding AI into operations, and just 1% claiming true AI maturity, most are still navigating the gray zone between human oversight and machine autonomy, leaders need to be clear eyed on requirements and how to build the trust contracts, governance models, and feedback loops that ensure decisions made by agents remain aligned with human intent and enterprise values.
This fast-hitting Brain Trust drills into the questions of how enterprises can balance autonomy, explainability, and accountability as early adopters have deployed/learned the hard lessons on getting agents to sense, decide, and act in real time.
